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Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting

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  • Matteo Barigozzi
  • Marc Hallin

Abstract

In large panels of financial time series with dynamic factor structure on the levels or returns, the volatilities of the common and idiosyncratic components often exhibit strong correlations, indicating that both are exposed to the same market volatility shocks. This suggests, alongside the dynamic factor decomposition of returns, a dynamic factor decomposition of volatilities or volatility proxies. Based on this observation, Barigozzi and Hallin (2016) proposed an entirely non-parametric and model-free two-step general dynamic factor approach which accounts for a joint factor structure of returns and volatilities, and allows for extracting the market volatility shocks. Here, we go one step further, and show how the same two-step approach naturally produces volatility forecasts for the various stocks under study. In an applied exercise, we consider the panel of asset returns of the constituents of the S&P100 index over the period 2000–2009. Numerical results show that the predictors based on our two-step method outperform existing univariate and multivariate GARCH methods, as well as static factor GARCH models, in the prediction of daily high–low range—while avoiding the usual problems associated with the curse of dimensionality.
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  • Matteo Barigozzi & Marc Hallin, 2015. "Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting," Working Papers ECARES ECARES 2015-22, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/200436
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    References listed on IDEAS

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    Cited by:

    1. Valentina Aprigliano & Lorenzo Bencivelli, 2013. "Ita-coin: a new coincident indicator for the Italian economy," Temi di discussione (Economic working papers) 935, Bank of Italy, Economic Research and International Relations Area.
    2. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    3. M. E. Bontempi & I. Mammi, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," Working Papers wp843, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Papers 1510.05118, arXiv.org, revised Jul 2016.
    5. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    6. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    7. Fabrizio Cipollini & Giampiero M. Gallo, 2018. "Modeling Euro STOXX 50 Volatility with Common and Market–specific Components," Working Paper series 18-26, Rimini Centre for Economic Analysis.
    8. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.

    More about this item

    Keywords

    volatility; dynamic factor models; GARCH models;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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